Abstract

<p>Earthquakes prediction is considered the holy grail of seismology. After almost a century of efforts without convincing results, the recent raise of machine learning (ML) methods in conjunction with the deployment of dense seismic networks has boosted new hope in this field. Even if large earthquakes still occur unanticipated, recent laboratory, field and theoretical studies support the existence of a preparatory phase preceding earthquakes, where small and stable ruptures progressively develop into an unstable and confined zone around the future hypocenter. The problem of recognizing the preparatory phase of earthquakes is of critical importance for mitigating seismic risk for both natural and induced events. Here, we focus on the induced seismicity at The Geysers geothermal field in California. We address the preparatory phase of M~4 earthquakes identification problem by developing a ML approach based on features computed from catalogues, which are used to train a Recurrent Neural Network (RNN). We show that RNN successfully reveal the preparation of M~4 earthquakes. These results confirm the potential of monitoring induced microseismicity and should encourage new research also in predictability of natural earthquakes.</p>

Highlights

  • The physics of earthquake initiation is a challenging research field with severe implications for modern society in terms of earthquake forecasting and seismic risk mitigation.Pioneering studies [1–7] have shown that large magnitude earthquakes can be anticipated by foreshocks and slip instabilities

  • For the first time, the use of recurrent neural networks (RNN) to capture the preparatory phase of moderate earthquakes by studying the spatio-temporal evolution of micro-seismicity

  • We explored if RNN can detect and exploit the complex pattern of different potential seismic precursors to unveil the preparatory processes of larger magnitude induced earthquakes

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Summary

Introduction

Pioneering studies [1–7] have shown that large magnitude earthquakes can be anticipated by foreshocks and slip instabilities. The non-systematic foreshock appearance and the lack of systematic precursory patters led the background physical processes generating foreshocks and the preparatory phase being not fully understood and matter of debate [8,9]. Two main contrasting models have been proposed concerning foreshocks generations. According to some authors [9–11], foreshocks are related to a tectonic loading process associated to aseismic slip, which represents a deviation from the normal behavior of seismicity [12]. This model would support the existence of a preparatory phase for large earthquakes, leaving us with the hope that in future earthquakes will be predictable

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